Tiny YOLOv2

This model is a real-time neural network for object detection that detects 20 different classes. It is made up of 9 convolutional layers and 6 max-pooling layers and is a smaller version of the more complex full YOLOv2 network.

**Paper**
[YOLO9000: Better, Faster, Stronger][1]
**Dataset**
The Tiny YOLO model was trained on the Pascal VOC [dataset.][2]
**Source**
The model was converted from a Core ML version of Tiny YOLO using [ONNXMLTools][4]. The source code can be found [here][5]. The Core ML model in turn was converted from the [original network][6] implemented in Darknet (via intermediate conversion through Keras).
**Model input and output**
**Input**
* Input image of the shape (3x416x416)
**Output**
* Output is a (1x125x13x13) array
**Pre-processing steps**
Resize the input image to a (3x416x416) array of type float32.
**Post-processing steps**
The output is a (125x13x13) tensor where 13x13 is the number of grid cells that the image gets divided into. Each grid cell corresponds to 125 channels, made up of the 5 bounding boxes predicted by the grid cell and the 25 data elements that describe each bounding box (5x25=125). For more information on how to derive the final bounding boxes and their corresponding confidence scores, refer to this [post][7].
**Sample test data**
Sets of sample input and output files are provided in .npz format (test_data_*.npz). The input is a (3x416x416) numpy array of a test image from Pascal VOC, while the output is a numpy array of shape (1x125x13x13).
**Model size: 5.3 MB**
**License**
[MIT][8]
[1]: https://arxiv.org/pdf/1612.08242.pdf
[2]: http://host.robots.ox.ac.uk/pascal/VOC/
[3]: https://pjreddie.com/darknet/yolov2/
[4]: https://github.com/onnx/onnxmltools
[5]: https://github.com/hollance/YOLO-CoreML-MPSNNGraph
[6]: https://pjreddie.com/darknet/yolov2/
[7]: http://machinethink.net/blog/object-detection-with-yolo/
[8]:https://github.com/onnx/models/blob/master/LICENSE